Dynamic Curvature Constrained Path Planning

TL;DR

This paper introduces the Dynamic Curvature-Constrained Path Planning Algorithm (DCCPPA) for 2D environments, comparing its performance with RRT and PRM to demonstrate its effectiveness in constrained path planning tasks.

Contribution

The paper presents a novel path planning algorithm, DCCPPA, specifically designed for 2D spaces with curvature constraints, and provides a comparative analysis with existing methods.

Findings

DCCPPA effectively navigates constrained environments.

Compared to RRT and PRM, DCCPPA shows improved path optimality.

The algorithm demonstrates versatility across various applications.

Abstract

Effective path planning is a pivotal challenge across various domains, from robotics to logistics and beyond. This research is centred on the development and evaluation of the Dynamic Curvature-Constrained Path Planning Algorithm (DCCPPA) within two dimensional space. DCCPPA is designed to navigate constrained environments, optimising path solutions while accommodating curvature constraints.The study goes beyond algorithm development and conducts a comparative analysis with two established path planning methodologies: Rapidly Exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM). These comparisons provide insights into the performance and adaptability of path planning algorithms across a range of applications.This research underscores the versatility of DCCPPA as a path planning algorithm tailored for 2D space, demonstrating its potential for addressing real-world path planning challenges across various domains. Index Terms Path Planning, PRM, RRT, Optimal Path, 2D Path Planning.